Design Of Knowledge-based System Research Articles

Digital design and manufacturing of wood head golf club in a cyber physical environment

PurposeThe purpose of this paper is to establish a cyber physical environment for digital product design and manufacturing. To realize this goal, the specific issue of integrating design knowledge-based system (KBS) and 3D printing (3DP) system is focused. A graphics generation method is thereby developed to transform the KBS outputs into graphical format which can be directly read and manufactured by 3DP system.Design/methodology/approachA graphics generation method is proposed in this paper. Through organizing alphanumeric outputs of the consultation session with a design KBS into parametric format, the consultation results can be directly used by computer-aided design (CAD) tools to generate graphical models which can be further exported into a 3DP system to produce physical objects.FindingsThe proposed graphics generation method can be effective to link design KBS and 3DP. Therefore, the seamless connection between design and prototyping systems can be realized, which further lays the communication foundation for a cyber physical environment for digital design and manufacturing.Originality/valueThis study provides research insights about potential cyber physical system applications in digital design and manufacturing area. Moreover, this paper contributes an effective technique to integrate design KBS and 3DP.

A Knowledge-based Parametric Design System for Trimming Dies

In this paper, by analyzing the structure characteristics of trimming dies, and combining with the theory of knowledge engineering, the author expounds the system architecture of the knowledge-based parametric design system and the design methods and key points of its main modules design. At present, this system has been put into practice and has achieved good benefits.

Towards generative systems for supporting product design

Various advanced computational, cognitive and innovative approaches have been developed in the last three decades to advance the fields of artificial intelligence and knowledge-based design systems. This paper reviews the development of generative design systems in the literature and of those developed by the author. It presents a framework of a generative design system with several real design examples. Finally, the paper examines the future direction for the advancement of generative design. A generative design system supports the generation and exploration of a large number of alternative design solutions, using automatic transformation algorithms of AI nature. It maintains a consistent style of all the explored solutions, but with design features and variables. It can also include an evaluation mechanism in the generative process for the system to search for the potentially optimum design solutions.

A qualitative inference method for prediction of geographic process using spatial and temporal relations

Knowledge engineering is the systematic self-disciplined set of activities involved in design and development of knowledge-based systems. Abstraction of the real world into codified information is the objective of knowledge representation (KR). The survey of various works of researchers in the domain of spatio-temporal knowledge representation for reasoning real world actions in geographic applications is presented. Geographic event attributed spatial entity (GEASE) knowledge-base that will aid in reasoning events causing the spatial change in geographic features over the period of time is proposed. GEASE knowledge-base describing a spatial entity by considering the temporal event as an attribute is constructed to infer events that cause geographical changes. As a result, the significance of the proposed knowledgebase is illustrated by evaluating event based queries on synthetic dataset. Finally, we conclude the work with direction to enhance the logical formulation for its implementation on real datasets in future.

Eliciting and Representing High-Level Knowledge Requirements to Discover Ecological Knowledge in Flower-Visiting Data.

Observations of individual organisms (data) can be combined with expert ecological knowledge of species, especially causal knowledge, to model and extract from flower–visiting data useful information about behavioral interactions between insect and plant organisms, such as nectar foraging and pollen transfer. We describe and evaluate a method to elicit and represent such expert causal knowledge of behavioral ecology, and discuss the potential for wider application of this method to the design of knowledge-based systems for knowledge discovery in biodiversity and ecosystem informatics.

In Memoriam: Dr. Clive L. Dym (1942–2016)

In Memoriam: Dr. Clive L. Dym (1942–2016)

Enhancing learning effectiveness by adopting a knowledge-based usability guidelines

Purpose – Guidelines for the design of knowledge-based e-learning usability systems are absent from the current recognized set of usability design heuristics and from an established evaluation methodology of e-learning system developments. Such systems can help Web designers and instructional designers design for different user needs and decide which properties are of a higher priority, thus meriting more design and development efforts. The authors aim to help students develop higher-order thinking skills, such as application, evaluation and syntheses of knowledge. Design/methodology/approach – The authors applied Merrill ' s first principles of instruction and usability properties as pedagogical and usability design guidelines, knowledge management (KM) and hierarchical task analysis as methodological knowledge bases. The authors proposed a KM e-learning usability framework which frames our mapping of Web usability attributes to e-learning usability properties. The authors aim to investigate whether adopting Merrill ' s first principles of instruction and usability properties as knowledge-based guidelines/design factors would help learners develop higher-order thinking skills and whether this design would result in positive technology acceptance. The authors also developed a method matrix to map the selected methods of cognitive engineering to its potential uses in the KM e-learning usability framework of this paper and mapped e-learning usability tools with components in the KM e-learning usability system. Findings – Findings indicated that our design effectively helped learners to demonstrate higher-order thinking skills and positive technology acceptance, promising indications toward the design and development of knowledge-based usability frameworks and systems. Research/limitations/implications – The sample size of this paper is small. Hence, conclusions are not generalizable at this moment. Originality/Value – The authors’ contributions are twofold: First, the authors proposed a KM e-learning usability framework, which frames the mapping of KM processes to e-learning principles and usability properties. Second, the authors proposed a method matrix which maps the selected methods of cognitive engineering to its potential uses in their KM e-learning usability framework. Based on these mappings and focusing on the usability properties navigation and learning support, the authors used ICT/Web2.0 tools to present/visualize information more clearly and more sensibly/manageably to students, to help trigger new knowledge and develop higher-order thinking skills, such as application, evaluation and syntheses of knowledge and articulate information from different perspectives throughout the KM life cycle.

Conceptual design of biomass-fired cogeneration plant through a knowledge-based system

Industries such as sugar/ethanol and paper/pulp generate large amount of biomass as by-products that must be properly handled to avoid environmental damage. A synergetic alternative to address this issue is to use the residual biomass as fuel in a cogeneration plant. In this work, it is presented the development of a knowledge-based system for conceptual design of biomass cogeneration plants. Elicitation from experts and technical literature provides knowledge for the base. A knowledge representation scheme composed of rules, object-oriented and semantic nets because it is flexible enough to cope with future knowledge base expansion. For rapid prototyping, a shell is used to implement the knowledge base. As a result, the system is able to quickly provide high quality conceptual solutions of biomass-fired cogeneration plants to support the designer in his/her decision making.

A Novel Rapid Conceptual Design Based on Knowledge Reuse and Evolution

Designers often have difficulties in fulfilling rapid conceptual design for complex products or system due to the lack of sufficient multi-disciplinary well-structured and well-evolved knowledge. Therefore, this paper presents a knowledge-based multi-disciplinary system for the conceptual design by reusing the knowledge and synthesizing the evolutionary design methodology (EDM). With the system, the designers can easily search the existing design data and successful cases for reusing or evolving the functions and parameters for the design to meet requirements. decomposing based on the knowledge. Evolutionary design methodology (EDM) originates from the regulation of the nature and corresponds to the rule of human cognitive competence. The process of the rapid conceptual design becomes easier for designers and customers to collaborate, which benefits from its advantages of one-sided to comprehensive and from abstract to detailed. This paper introduces EDM for rapid conceptual design and a tool to display it. With the visualized chart, the EDM is easy to be mastered and applied to research for designers. Some related works are discussed in Section II. Our evolutionary design methodology is presented in Section III. Section IV implements our approach through conceptual design software. And Section V concludes this paper.

Development of a knowledge-based system for cogeneration plant design: Verification, validation and lessons learned

This paper presents the development of a knowledge-based system (KBS) prototype able to design natural gas cogeneration plants, demonstrating new features for this field. The design of such power plants represents a synthesis problem, subject to thermodynamic constraints that include the location and sizing of components. The project was developed in partnership with the major Brazilian gas and oil company, and involved interaction with an external consultant as well as an interdisciplinary team. The paper focuses on validation and lessons learned, concentrating on important aspects such as the generation of alternative configuration schemes, breadth of each scheme description created by the system, and its module to support economic feasibility analysis.

Development and implementation of a knowledge based TBM tunnel segment lining design program

본 논문에서는 인공신경망 기술을 이용한 TBM 터널 세그먼트 라이닝의 설계 시스템 개발에 관한 내용을 다루었다. 먼저 개발 시스템에 대한 개념 및 개발 과정과 시스템을 구성하는 각 요소기술 및 개별 모듈 개발에 관한 내용을 기술하였다. 본 시스템의 요소기술인 ANN-기반의 세그먼트 라이닝 부재력 예측 시스템에 대해 그 개념과 ANN 학습과정 및 검증과정을 기술하였다. ANN-기반의 세그먼트 라이닝 부재력 예측은 유한요소해석을 토대로 구축한 DB를 ANN을 통해 일반화 한 후 개발된 엔진을 세부 모듈에 접목시켜 별도의 해석 없이 유사 단면 혹은 현장에 적용이 가능하도록 하였다. 또한 해석 대상 단면에 대하여 상용 유한요소해석 프로그램과 연계하여 해석 Input파일의 자동생성이 가능하도록 하였으며 유한요소해석 결과를 통한 단면 검토가 가능하도록 하였다. This paper concerns the development of a knowledge-based tunnel design system within the framework of artifical neural networks(ANNs). The system is aimed at expediting a routine tunnel design works such as computation of segment lining body forces and stability analysis of selected cross section. A number of sub-modules for computation of segment lining body forces and stability analysis were developed and implemented to the system. It is shown that the ANNs trained with the results of 3D numerical analyses can be generalized with a reasonable accuracy, and that the ANN based tunnel design concept is a robust tool for tunnel design optimization. The details of the system architecture and the ANNs development are discussed in this paper.

Incorporating quality function deployment and analytical hierarchy process in a knowledge-based system for automotive production line design

The authors present an intelligent tool to perform the design of automotive production line, specifically for automotive body-in-white panels. The system adopts quality function deployment principle and analytical hierarchy process methodology to be the main reasoning logic for design decisions, and to explore the effect of enhancing these traditional tools with an artificial intelligent scheme, namely the knowledge-based system. Moreover, all knowledge and expertise of production line design are stored in an interactive knowledge base. Thus, the system is knowledge-base-oriented and exhibits the ability to repetitively deal with design problems as changes occur to design needs or manufacturing process options. Furthermore, this technique offers two knowledge bases: the first holds the production requirements and their correlations to essential process attributes, while the second contains available manufacturing processes and their characteristics to fabricate body-in-white panels. Finally, the work contains a case study to verify the developed system's functionality and merits. The results demonstrate an increase in the design procedure efficiency and reduction in the inconsistency. Similarly, the system provides improved results interpretation using graphical representation, in addition to the possibility of tracking the justification of final selected manufacturing processes down to the customer requirements.

Knowledge-based functional conceptual design: Model, representation, and implementation

Conceptual design is thus crucial to define new creative products. During conceptual design process, knowledge derivate from different domains is required to achieve a creative design. However, most of the design models restrict their representation to a specific domain. Knowledge representation for multidisciplinary is still an incipient issue for engineering design researchers. Moreover, to increase the robustness of design resolutions is another problem obsessing designers. This article proposes an adapted Gero’s Function–Behavior–State model (Requirement–Function–Behavior–Principle solutions) to aid creative design activities during conceptual design stage by introducing an integrated cross-domain knowledge representation methodology, to increase robustness by reusing existing cases knowledge through retrieval algorithm, and to realize effectiveness by putting forward a design synthesis methodology. Finally, a prototype system named knowledge-based innovative conceptual design system is developed to implement our approaches, and effectiveness of the proposed model is illustrated with a detailed example related to the design of eye movement apparatus incorporated with oculomotor machine of vision prosthesis for acquired blindness, with an overall design procedure classified.

When innovating, go slow [Spectral Lines

THE TANGLED HISTORY of innovation reveals a peculiar lesson: Slow is often better than fast. The current assumption is that innovation at its best hits like a hurricane. Austrian-American economist and political scientist Joseph Schumpeter, who first recognized the importance of innovation for economic growth, famously described innovation as coming in gales, sweeping aside all that came before. But whether it's biomedical, digital, or electromechanical, systems-level innovation requires human ingenuity, even wisdom. And the wise adaptation of advances in technoscience—in the design, engineering, and management of large knowledge-based systems that deliver energy, information, transportation, security, food, and health—takes time.

A Knowledge Based Approach for Product Variant Design

In order to solve the problem that design resources can not be effectively reused, variant design is used in engineering field. Knowledge is the key factor implementing strategy of variant design. This paper puts forward the knowledge classification, process model, information resource base and reasoning model for variant design. Knowledge of variant design is classified into product domain knowledge, variant design methodology and basis theory knowledge, variant design process combines top-down mode to bottom-top mode, its corresponding information resource base includes case base, knowledge base and database. CBR is the core for design reasoning. The frame of knowledge-based variant design system includes resource base, resource management, variant design and evaluation subsystem. Using the system, designer could reuse design resource and reduce developing cost.

A survey on knowledge-based systems for ship design and ship operation

The paper gives a survey of knowledge-based systems (KBS) for ship design and ship operation over the past three decades. While there are several commercial applications of KBS in ship operation, applications in ship design have remained almost exclusively in the academic stage.

Integrated Knowledge-Based System for Machine Design

An integrated design system (IDS) approach has been developed to integrate various stages of the mechanical design process, including rapid prototyping. The system consist, of design, analysis, calculation, rapid prototyping, and library modules, and it blends artificial intelligence methods, CAD-CAM, and technical computing packages into a single environment. The system has been applied for the design of one-stage gearbox with helical gear. In this case study, all stages of the design process are carried out by using IDS.

Reusable Components in Knowledge-based Configuration Design Systems

This paper takes a look on how components for knowledge-based intelligent systems can be created for reuse. For this purpose, we use production rules as inspiration for a system that uses an ontology description for the method and the domain ontology for the knowledge about the domain the problem takes place in. In this paper we give a description of an approach that hopefully can give insight into such a system. The approach is based on previous work and other scientific publications concerning this field of study. The created ontology models are in no way guaranteed to be useful outside of this example and the approach itself might still need to be improved in the future.

Chapter 1: Biomedical Knowledge Integration

The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems.

In Silico Assessment of Adverse Effects of a Large Set of 6-Fluoroquinolones Obtained from a Study of Tuberculosis Chemotherapy

Among the different chemotherapeutic classes available today, the 6-fluoroquinolone (6-FQ) antibacterials are still one of the most effective cures in fighting tuberculosis (TB). Nowadays, the development of novel 6-FQs for treatment of TB mainly depends on understanding how the structural modifications of the main quinolone scaffold at specific positions affect the anti-mycobacterial activity. Alongside the structure-activity relationship (SAR) studies of the 6-FQ antibacterials, which can be considered as a golden rule in the development of novel active antitubercular 6-FQs, the structure side effects relationship (SSER) of these drugs must be also taken into account. In the present study we focus on a proficient implementation of the existing knowledge-based expert systems for design of novel 6-FQ antibacterials with possible enhanced biological activity against Mycobaterium tuberculosis as well as lower toxicity. Following the SAR in silico studies of the quinolone antibacterials against M. tuberculosis performed in our laboratory, a large set of 6-FQs was selected. Several new 6-FQ derivatives were proposed as drug candidates for further research and development. The 6- FQs identified as potentially effective against M. tuberculosis were subjected to an additional SSER study for prediction of their toxicological profile. The assessment of structurally-driven adverse effects which might hamper the potential of new drug candidates is mandatory for an effective drug design. We applied publicly available knowledge-based (expert) systems and Quantitative Structure-Activity Relationship (QSAR) models in order to prepare a priority list of active compounds. A preferred order of drug candidates was obtained, so that the less harmful candidates were identified for further testing. TOXTREE expert system as well as some QSAR models developed in the framework of EC funded project CAESAR were used to assess toxicity. CAESAR models were developed according to the OECD principles for the validation of QSAR and they turn to be appropriate tools for in silico tests regarding five different toxicity endpoints. Those endpoints with high relevance for REACH are: bioconcentration factor, skin sensitization, carcinogenicity, mutagenicity, and developmental toxicity. We used the above-mentioned freely available models to select a set of less harmful active 6-FQs as candidates for clinical studies.